A rotorcraft-based unmanned aerial vehicle exhibits more complex properties compared to its full-size counterparts due to its increased sensitivity to control inputs and disturbances and higher bandwidth of its dynamics. As an aerial vehicle with vertical take-off and landing capability, the helicopter specifically poses a difficult problem of transition between forward flight and unstable hover and vice versa. The LPV control technique explicitly takes into account the change in performance due to the real-time parameter variations. The technique therefore theoretically guarantees the performance and robustness over the entire operating envelope. In this study, we investigate a new approach implementing model identification for use in the LPV control framework. The identification scheme employs recursive least square technique implemented on the LPV system represented by dynamics of helicopter during a transition. The airspeed as the scheduling of parameter trajectory is not assumed to vary slowly. The exclusion of slow parameter change requirement allows for the application of the algorithm for aggressive maneuvering capability without the need of expensive computation. The technique is tested numerically and will be validated in the autonomous flight of a small scale helicopter.
Institute of Embedded Systems（嵌入式系统研究所）；Zurich University of Applied Sciences（苏黎世应用科学大学）
Stand-alone functions additional to a UAV flight-controller, such as safety-relevant flight-path monitoring or payload-monitoring and control, may be SORA-required or advised for specific flight paths of delivery-drones. These functions, articulated as discrete electronic components either internal or external to the main fuselage, can be networked with other on-board electronics systems. Such an integration requires respecting the integrity levels of each component on the network both in terms of function and in terms of power-supply. In this body of work we detail an intra-component communic…
This work proposes a systematic framework for modelling and controller design of a Commercial-Off-The Shelf (COTS) unmanned rotorcraft using control theory and principles, for brick wall construction. With point to point navigation as the primary application, command velocities in the three axes of the Unmanned Aerial Vehicle (UAV) are considered as inputs of the system while its actual velocities are system outputs. Using the sine and step response data acquired from a Hardware-in-Loop (HiL) test simulator, the considered system was modelled in individual axes with the help of the proposed fr…
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a more robust technique to distinguish and track dynamic obstacles from static ones with only point cloud input. Then, to achieve dynamic avoidance, we propose the forbidden pyramids method to solve the desired vehicle velocity with an efficient sampling-based method in iteration. The motion primitives are generated by solving a nonlinear optimization problem with the constraint of desired velocity and the waypoint. Furthermor…
Collaborative target-tracking control using multiple autonomous fixed-wing UAVs with constant speeds
Department of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电子工程系）；Universidad Complutense de Madrid（马德里complutense大学）；Research School of Electrical, Energy and Material Engineering, Australian National University（澳大利亚国立大学电气、能源和材料工程研究学校）；Optus-Curtin Centre of Excellence in Artificial Intelligence, Curtin University（Curtin大学人工智能卓越中心）
As unmanned aerial vehicles (UAVs) become more accessible with a growing range of applications, the potential risk of UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. However, the coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras - a problem known as re-identification (reID). While there has been extensive research on person and vehicle reID to match objects across time and viewpoints, to the best of our knowledge, there ha…
This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream CNN delivering exploratory agency and an InceptionV3-based biometric LRCN for individual animal identification. We evaluate the perform…
CSAIL, MIT（麻省理工学院计算机科学与人工智能实验室）；University of Maryland（马里兰大学）；Virginia Tech（弗吉尼亚理工大学）
Our work is motivated by environmental monitoring tasks, where finding the global maxima (i.e., hotspot) of a spatially varying field is crucial. We investigate the problem of identifying the hotspot for fields that can be sensed using an Unmanned Aerial Vehicle (UAV) equipped with a downward-facing camera. The UAV has a limited time budget which it can use for learning the unknown field and identifying the hotspot. Our contribution is to show how this problem can be formulated as a novel multi-fidelity variant of the Gaussian Process (GP) multi-armed bandit problem. The novelty is two-fold: (…
Khalifa University（哈利法大学）；Kingston University London（伦敦金斯顿大学）
Control performance of Unmanned Aerial Vehicles (UAVs) is directly affected by their ability to estimate their states accurately. With the increasing popularity of autonomous UAV solutions in real world applications, it is imperative to develop robust adaptive estimators that can ameliorate sensor noises in low-cost UAVs. Utilizing the knowledge of UAV dynamics in estimation can provide significant advantages, but remains challenging due to the complex and expensive pre-flight experiments required to obtain UAV dynamic parameters. In this paper, we propose two decoupled dynamic model based Ext…
Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input. Then, a computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method. The approach is able to avoid both static obstacles and dynamic ones in the same framework. For static and dynamic obstacles, the collision check and motion constraints are different, and …
Swiss Federal Institute of Technology (ETH) Zürich（苏黎世联邦理工学院）
As off-the-shelf (OTS) autopilots become more widely available and user-friendly and the drone market expands, safer, more efficient, and more complex motion planning and control will become necessary for fixed-wing aerial robotic platforms. Considering typical low-level attitude stabilization available on OTS flight controllers, this paper first develops an approach for modeling and identification of the control augmented dynamics for a small fixed-wing Unmanned Aerial Vehicle (UAV). A high-level Nonlinear Model Predictive Controller (NMPC) is subsequently formulated for simultaneous airspeed…
ISCTE – Instituto Universitário de Lisboa（ISCTE – 里斯本大学学院）；PDMFC；IT – Instituto de Telecomunicações（IT – 电信研究所）；FCT – Universidade Nova de Lisboa（FCT – 新里斯本大学）；CTTC - Centre Tecnològic de Telecomunicacions de Catalunya (CERCA)（CTTC - 加泰罗尼亚电信技术中心（CERCA））
Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations and decrease UAV control communication performance in Air-to-Ground (A2G) links. Operating under the assumption that the 5G UAV communications infrastructure will never be entirely secure, we propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs. Our proposed solution uses two observable parameters: the Signal-to-Interference-plus-Noise Ratio (SINR) and the …
Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by a wide range of applications due to the availability of low-cost on-board sensors and computers. Tuning such systems to work properly requires extensive domain specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits of UAV based visual servoing is difficult due to the complexity of the models used. In this paper, we propose a novel noise tolerant approach for real-time identification and tuning of visual servoing systems, based on deep neural networks (DNN) classifi…
New Jersey Institute of Technology（新泽西理工学院）；University of Connecticut（康涅狄格大学）
Unmanned Aerial Vehicles(UAVs) are attaining more and more maneuverability and sensory ability as a promising teleoperation platform for intelligent interaction with the environments. This work presents a novel 5-degree-of-freedom (DoF) unmanned aerial vehicle (UAV) cyber-physical system for aerial manipulation. This UAV's body is capable of exerting powerful propulsion force in the longitudinal direction, decoupling the translational dynamics and the rotational dynamics on the longitudinal plane. A high-level impedance control law is proposed to drive the vehicle for trajectory tracking and i…
Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance
Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）；Thrust of Robotics and Autonomous Systems, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统研究所）；International Digital Economy Academy, Shenzhen, China（深圳国际数字经济学院）
We propose a framework that allows a mobile robot to build a map of an indoor scenario, identifying and highlighting objects that may be considered a hindrance to people with limited mobility. The map is built by combining recent developments in monocular SLAM with information from inertial sensors of the robot platform, resulting in a metric point cloud that can be further processed to obtain a mesh. The images from the monocular camera are simultaneously analyzed with an object recognition neural network, tuned to detect a particular class of targets. This information is then processed and i…
AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding
Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（创新工程学院工程科学系，澳门科学技术大学）；State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences（复杂系统管理与控制国家重点实验室，中国科学院自动化研究所）；State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences（复杂系统管理与控制国家重点实验室，中国科学院）
The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. W…
Jinan University（暨南大学）；University of Tunis El Manar（突尼斯马纳尔大学）；National Institute of Applied Sciences and Technology, University of Carthage（迦太基大学应用科学与技术国家研究所）；Prince Sultan University（苏丹王子大学）；CISTER/INESC TEC；ISEP-IPP（波尔图理工学院工程学院）；National School of Electronics and Telecommunication（国立电子与电信学院）
In the last few years, Unmanned Aerial Vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial fields. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over…
RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification
School of Information Science and Engineering, Zhejiang Sci-Tech University（浙江理工大学信息科学与工程学院）；School of Communication and Information Engineering, Hangzhou Dianzi University（杭州电子科技大学通信与信息工程学院）；College of Electronic Engineering, National University of Defense Technology（国防科技大学电子工程学院）
School of Electric Engineering and Informatics Institut Teknologi Bandung（万隆理工学院电气工程与信息学院）
This paper proposes an autopilot system that can be used to control the small scale rotorcraft during the flight test for linear-frequency-domain system identification. The input frequency swept is generated automatically as part of the autopilot control command. Therefore the bandwidth coverage and consistency of the frequency swept is guaranteed to produce high quality data for system identification. Beside that we can set the safety parameter during the flight test (maximum roll or pitch value, minimum altitude, etc) so the safety of the whole flight test is guaranteed. This autopilot for a…
AeroResQ: Edge-Accelerated UAV Framework for Scalable, Resilient and Collaborative Escape Route Planning in Wildfire Scenarios
Indian Institute of Science（印度科学研究院）；Information Sciences Institute, University of Southern California（信息科学研究所，南加州大学）；Renaissance Computing Institute, University of North Carolina（复兴计算研究所，北卡罗来纳大学）；University of Massachusetts（马萨诸塞大学）
With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The pro…
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
National Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统国家重点实验室）
The Hong Kong University of Science and Technology（香港科技大学）；National University of Singapore（新加坡国立大学）；University of British Columbia（不列颠哥伦比亚大学）
In many specific scenarios, accurate and effective system identification is a commonly encountered challenge in the model predictive control (MPC) formulation. As a consequence, the overall system performance could be significantly weakened in outcome when the traditional MPC algorithm is adopted under those circumstances when such accuracy is lacking. This paper investigates a non-parametric closed-loop behavior learning method for multi-agent motion planning, which underpins a data-driven predictive control framework. Utilizing an innovative methodology with closed-loop input/output measurem…
